Daniel Ting | DeepAI

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∙ 02/04/2023

Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting

Data sketching is a critical tool for distinct counting, enabling multis...

0 Jonathan Hehir, et al. ∙

∙ 03/29/2022

(Nearly) All Cardinality Estimators Are Differentially Private

We consider privacy in the context of streaming algorithms for cardinali...

0 Charlie Dickens, et al. ∙

∙ 07/10/2021

Few-Shot Domain Adaptation with Polymorphic Transformers

Deep neural networks (DNNs) trained on one set of medical images often e...

6 Shaohua Li, et al. ∙

∙ 04/11/2021

Simple, Optimal Algorithms for Random Sampling Without Replacement

Consider the fundamental problem of drawing a simple random sample of si...

0 Daniel Ting, et al. ∙

∙ 11/20/2020

HyperLogLog (HLL) Security: Inflating Cardinality Estimates

Counting the number of distinct elements on a set is needed in many appl...

0 Pedro Reviriego, et al. ∙

∙ 07/07/2020

Manifold Learning via Manifold Deflation

Nonlinear dimensionality reduction methods provide a valuable means to v...

0 Daniel Ting, et al. ∙

∙ 05/05/2020

Conditional Cuckoo Filters

Bloom filters, cuckoo filters, and other approximate set membership sket...

0 Daniel Ting, et al. ∙

∙ 02/15/2020

Security of HyperLogLog (HLL) Cardinality Estimation: Vulnerabilities and Protection

Count distinct or cardinality estimates are widely used in network monit...

0 Pedro Reviriego, et al. ∙

∙ 11/09/2018

Count-Min: Optimal Estimation and Tight Error Bounds using Empirical Error Distributions

The Count-Min sketch is an important and well-studied data summarization...

0 Daniel Ting, et al. ∙

∙ 03/06/2018

On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding

We develop theory for nonlinear dimensionality reduction (NLDR). A numbe...

0 Daniel Ting, et al. ∙

∙ 11/27/2017

Family learning: nonparametric statistical inference with parametric efficiency

Hypothesis testing and other statistical inference procedures are most e...

0 William Fithian, et al. ∙

∙ 09/12/2017

Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation

We introduce and study a new data sketch for processing massive datasets...

0 Daniel Ting, et al. ∙

∙ 09/06/2017

Optimal Sub-sampling with Influence Functions

Sub-sampling is a common and often effective method to deal with the com...

0 Daniel Ting, et al. ∙

∙ 08/16/2017

Adaptive Threshold Sampling and Estimation

Sampling is a fundamental problem in both computer science and statistic...

0 Daniel Ting, et al. ∙

∙ 03/15/2012

Online Semi-Supervised Learning on Quantized Graphs

In this paper, we tackle the problem of online semi-supervised learning ...

0 Michal Valko, et al. ∙

∙ 01/28/2011

An Analysis of the Convergence of Graph Laplacians

Existing approaches to analyzing the asymptotics of graph Laplacians typ...

0 Daniel Ting, et al. ∙

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